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example_training_of_ANN.py File Reference
Namespaces
example_training_of_ANN
Variables
string
example_training_of_ANN.problem_name
= "peaks"
LOAD DATA ############################ enter data set information.
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string
example_training_of_ANN.filename_data
= "./data/peaks.csv"
int
example_training_of_ANN.input_dim
= 2
int
example_training_of_ANN.output_dim
= 1
bool
example_training_of_ANN.scaleInput
= True
bool
example_training_of_ANN.normalizeOutput
= True
example_training_of_ANN.data
= np.loadtxt(open(filename_data, "rb"), delimiter=",")
example_training_of_ANN.X
=
data
[:, :-output_dim]
example_training_of_ANN.y
=
data
[:, input_dim:]
example_training_of_ANN.X_norm
=
utils.scale
(
X
, scaleInput)
example_training_of_ANN.y_norm
=
utils.normalize
(y, normalizeOutput)
example_training_of_ANN.x_train
example_training_of_ANN.x_val
example_training_of_ANN.y_train
example_training_of_ANN.y_val
example_training_of_ANN.test_size
example_training_of_ANN.n_train
= x_train.shape[0]
string
example_training_of_ANN.output_folder
= "./data/Output/"
SET PARAMETERS ############################ output filename.
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string
example_training_of_ANN.filename_out
= output_folder + problem_name
list
example_training_of_ANN.network_layout
= [10, 10]
string
example_training_of_ANN.activation_function
= 'relu'
string
example_training_of_ANN.activation_function_out
= 'linear'
float
example_training_of_ANN.learning_rate
= 0.001
example_training_of_ANN.kernel_regularizer
= tf.keras.regularizers.l2(l=0.0001)
string
example_training_of_ANN.kernel_initializer
= 'he_normal'
string
example_training_of_ANN.optimizer
= 'adam'
int
example_training_of_ANN.epochs
= 100
int
example_training_of_ANN.batch_size
= 128
int
example_training_of_ANN.random_state
= 1
example_training_of_ANN.model
= tf.keras.Sequential()
BUILD MODEL ############################.
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example_training_of_ANN.loss
example_training_of_ANN.metrics
example_training_of_ANN.training_time
= time.time()
TRAINING ############################.
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example_training_of_ANN.history
example_training_of_ANN.y_pred
= model.predict(X_norm)
SAVE MODEL ############################.
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feedforward neural network
training
keras
example_training_of_ANN.py
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